Constructing and evaluating core inflation measures from component‐level inflation data

This paper undertakes a comprehensive examination of 10 measures of core inflation and evaluates which measure produces the best forecast of headline inflation out‐of‐sample. We use the Personal Consumption Expenditure Price Index as our measure of inflation. We use two sets of components (17 and 50) of the Personal Consumption Expenditure Price Index to construct these core inflation measures and evaluate these measures at the three time horizons (6, 12 and 24 months) most relevant for monetary policy decisions. The best measure of core inflation for both sets of components and over all time horizons uses weights based on the first principal component of the disaggregated (component‐level) prices. Interestingly, the results vary by the number of components used; when more components are used the weights based on the persistence of each component is statistically equivalent to the weights generated by the first principal component. However, those forecasts using the persistence of 50 components are statistically worse than those generated using the first principal component of 17 components. The statistical superiority of the principal component method is due to the fact that it extracts (in the first principal component) the common source of variation in the component level prices that accurately describes trend inflation over the next 6–24 months.     

Variations on a theme: Clifford’s parallelism in elliptic space

In 1873, W. K. Clifford introduced a notion of parallelism in the three-dimensional elliptic space that, quite surprisingly, exhibits almost all properties of Euclidean parallelism in ordinary space. The purpose of this paper is to describe the genesis of this notion in Clifford’s works and to provide a historical analysis of its reception in the investigations of F. Klein, L. Bianchi, G. Fubini, and E. Bortolotti. Special emphasis is placed upon the important role that Clifford’s parallelism played in the development of the theory of connections.     

大学生突发事件的应急处理与思考——以潍坊学院生物与农业工程学院为例

在学校组织开展的各项活动特别是体育比赛活动中,难免出现各类突发性事故。对突发事件的应急处理和善后工作,考验着高校各级领导特别是辅导员的工作能力。如何科学、有效地处理突发事件,避免及减少突发事件带来的损失,提升高校辅导员应对突发事件的能力和水平,具有重要的现实意义。     

避孕压力为啥由女性承担

<正>也不是没人研究过男性避孕药,只是面对这"野火烧不尽,春风吹又生"的精子们,科学家们绞尽脑汁却依旧所获不多。避孕,是男女两个人的事,但在选择避孕方式上,女性安装宫内节育器的比例要比男性做输精管结扎术的比例大得多,市面上的避孕药品也几乎都偏向于女性。像宫内节育器这样的避孕措施对女性的伤害还真不小。最可怕的是有很多女性即使带上宫内节育器还会有怀孕的情况,而这样的情况往往会导致宫外孕。     

Methods for backcasting,nowcasting and forecasting using factor‐MIDAS: With an application to Korean GDP

We utilize mixed‐frequency factor‐MIDAS models for the purpose of carrying out backcasting, nowcasting, and forecasting experiments using real‐time data. We also introduce a new real‐time Korean GDP dataset, which is the focus of our experiments. The methodology that we utilize involves first estimating common latent factors (i.e., diffusion indices) from 190 monthly macroeconomic and financial series using various estimation strategies. These factors are then included, along with standard variables measured at multiple different frequencies, in various factor‐MIDAS prediction models. Our key empirical findings as follows. (i) When using real‐time data, factor‐MIDAS prediction models outperform various linear benchmark models. Interestingly, the “MSFE‐best” MIDAS models contain no autoregressive (AR) lag terms when backcasting and nowcasting. AR terms only begin to play a role in “true” forecasting contexts. (ii) Models that utilize only one or two factors are “MSFE‐best” at all forecasting horizons, but not at any backcasting and nowcasting horizons. In these latter contexts, much more heavily parametrized models with many factors are preferred. (iii) Real‐time data are crucial for forecasting Korean gross domestic product, and the use of “first available” versus “most recent” data “strongly” affects model selection and performance. (iv) Recursively estimated models are almost always “MSFE‐best,” and models estimated using autoregressive interpolation dominate those estimated using other interpolation methods. (v) Factors estimated using recursive principal component estimation methods have more predictive content than those estimated using a variety of other (more sophisticated) approaches. This result is particularly prevalent for our “MSFE‐best” factor‐MIDAS models, across virtually all forecast horizons, estimation schemes, and data vintages that are analyzed.     

磁流变液中磁性颗粒成链与团聚的临界特征分析

热运动对纳米级磁流变液系统的颗粒团聚行为和宏观流变特性扰动影响不可忽视.为研究施加外磁场后系统微结构与热运动间的关系，探讨在不同颗粒粒径情况下热运动能量在系统总能量中的占比.采用CCD设计方法，分析Monte Carlo仿真获取的颗粒位形信息，生成关于外磁场磁感应强度、颗粒粒径和体积分数的系统热耦合系数<λ>回归模型;成链团聚的临界颗粒粒径计算值比文献经验值高出约23%，证实在考虑系统内多因素的综合影响下，纳米级磁流变液具有更严格的流变现象临界发生条件.     

基于小波变换的雷达目标回波信号提取算法

随着雷达技术的快速发展,低信噪比(signal-to-noise ratio, SNR)条件下的雷达目标信号提取越来越重要。传统的脉冲压缩算法在低SNR条件下作用不明显。提出了一种新的在低SNR条件下的雷达目标回波信号提取算法。该算法在传统脉冲压缩算法的基础之上,采用小波变换对脉冲压缩后的信号进行分解,提取信号高频信息的小波系数。再用改进的小波变换模极大值算法对信号的高频信息进行去噪,然后重构信号的高频信息,最后再重构完整的雷达目标回波信号。仿真结果表明,该算法能够有效地去除噪声,提取出雷达目标的回波信号,相比于脉冲压缩算法及其他算法性能得到了很大的提升。     

Hom-李三系上的乘积结构和复结构

本文主要从代数角度研究Hom-李三系上的两种几何结构,即积结构、复结构。分别给出了积结构和复结构存在时对应的Hom-李三系的分解。同时,也研究了一些特殊的积结构和复结构。最后,在这两种结构间增加一个兼容条件,得到了复积结构。